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https://github.com/raihan4520/ai-and-expert-system

Python implementations from the AI and Expert System course at AIUB, covering search algorithms, machine learning, and neural networks.
https://github.com/raihan4520/ai-and-expert-system

ai alpha-beta-pruning astar-algorithm bfs constraint-satisfaction-problem decision-trees dfs knn machine-learning minimax neural-networks python search-algorithms

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Python implementations from the AI and Expert System course at AIUB, covering search algorithms, machine learning, and neural networks.

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# Artificial Intelligence (AI) and Expert System Course Codes



This repository contains the code and assignments completed during the **Artificial Intelligence (AI)** course at **American International University - Bangladesh (AIUB)**. The repository serves as a collection of the various algorithms and techniques explored throughout the course.



### Course Information

For more details on the course, refer to the [AIUB Undergraduate Course Catalog](https://www.aiub.edu/faculties/fst/ug-course-catalog).  

*Note: Search for "Artificial Intelligence and Expert System" for specific course information.*



## Table of Contents

- [Overview](#overview)

- [Course Topics Covered](#course-topics-covered)

- [Technologies Used](#technologies-used)

- [How to Run](#how-to-run)

- [Contact](#contact)



## Overview



Throughout the AI course, I implemented several key algorithms and concepts that form the foundation of artificial intelligence. These codes include various classical AI techniques and machine learning approaches, which were part of the course curriculum.



The code in this repository demonstrates how core AI principles were applied to solve specific problems or exercises given during the course.



## Course Topics Covered



1. **Search Algorithms**  

   - Implementation of classical search algorithms like Breadth-First Search (BFS), Depth-First Search (DFS), and A*.

   - Focused on graph traversal, pathfinding, and heuristic-based search.



2. **Constraint Satisfaction Problems (CSP)**  

   - Solving constraint satisfaction problems such as the N-Queens problem using backtracking and forward checking techniques.



3. **Machine Learning Algorithms**  

   - Implementations of basic machine learning algorithms including:

     - **Decision Trees**

     - **Naive Bayes**

     - **K-Nearest Neighbors (KNN)**

   - Practical exploration of supervised learning techniques.



4. **Game Playing Algorithms**  

   - Using the Minimax algorithm and Alpha-Beta Pruning for decision-making in turn-based games like Tic-Tac-Toe.



5. **Basic Neural Networks**  

   - Constructing simple neural networks from scratch for basic classification tasks using backpropagation.



6. **Other AI Techniques**  

   - Exploration of additional AI-related algorithms and concepts such as optimization, probabilistic reasoning, and more.



## Technologies Used



- **Programming Language:** Python

- **Libraries Used:** 

  - NumPy (for numerical computation)

  - scikit-learn (for machine learning algorithms)

  - Matplotlib (for visualizing data and results)



## How to Run



To run the code provided in this repository:



1. Clone the repository:

   ```bash

   https://github.com/Raihan4520/AI-And-Expert-System.git

2. Navigate to the specific folder containing the code you want to execute.

3. Install the necessary libraries using the following command:

   ```bash

   pip install 

4. Run the Python script using:

   ```bash

   python .py



Each code may have a different purpose based on the topic covered in the course, so review the code comments for more detailed information.



## Contact



If you have any questions or suggestions, feel free to reach out through the repository's issues or contact me directly.